The condition of patients, particularly, in high dependency care or intensive care, is monitored in a variety of ways. For instance, vital signs such as one or more channels of electrocardiogram (ECG), respiration (for instance measured by electrical impedance pneumography), oxygen saturation (for instance measured by pulse oximetry with a finger probe), blood pressure and skin temperature may all be monitored. These may be regarded as “primary” signals, or parameters, which are measured directly. However, in addition, it is possible to derive from them some “secondary” parameters such as heart rate, heart rate variability, respiration rate and S-T segment elevation/depression (which is measured from the electrocardiogram). Typically the various parameters are collected at different rates, for instance the ECG at 256 Hz, the pulse oximeter signal at 81.3 Hz, the respiration at 64 Hz, the temperature at 1 Hz and blood pressure once every 10 or 20 minutes if measured non-invasively using a blood-pressure cuff. Further, the secondary parameters may be based on some averaging over a period of time.
It has been proposed, as shown in FIGS. 1 and 2 of the accompanying drawings, to display several of the measurements representing a patient's condition together using an integrated monitor. FIG. 1 illustrates a display showing many of the parameters mentioned above, and FIG. 2 illustrates a display of the heart rate and the heart rate variability. However, even with such a wealth of data available to the clinician (or possibly because of it), it can be difficult to see at a glance whether the patient's condition is normal, changing for the better, or, more seriously, for the worse.
In addition the clinical significance of changes of different degree in the different parameters may differ. For instance, a small percentage change in temperature may be much more significant than a small percentage change in blood pressure, or a change in respiration rate may be more significant than a similar change in heart rate. This relative significance may vary depending on the patient's medical problem. Further, the fact that a change in condition may be reflected in one or more parameters and in different ways for different patients and different medical conditions, means that it is very difficult to provide a satisfactory solution by, for instance, simply setting thresholds on each of the displayed parameters. A significant change in condition may be reflected by combinations of parameters, for instance decrease in heart rate combined with a decrease in blood pressure may be serious even though the values per se are not abnormal. It should be noted, though, that the early detection of deterioration in a patient's condition can significantly improve the clinical outcome, and reduce the need for later intensive care, which is thus beneficial both for the patient and for the clinician.
The present invention provides for the display of parameters representing a patient's condition in a simplified way, and which allows the changes in a patient's condition to be seen easily. For instance, the departure of a patient's condition from normality, defined either for that patient or for a group of patients, may be displayed, or equally the progress of a patient from an abnormal condition to a normal condition or vice versa.
In more detail the present invention provides apparatus for displaying a graphical representation of a patient's condition as measured by n parameters, where n>3, comprising a processor which maps data points represented by said n parameters from an n-dimensional measurement space into an m-dimensional visualisation space, where m<n, using a dimensionality reduction mapping, and a display which displays the visualisation space and the data points mapped into it, and which is adapted to the display of dynamically changing values of said parameters by means of the mapping being carried out by a trained artificial neural network.
The parameters may be primary signals as mentioned above, or secondary parameters derived from them. For instance, they may be a respiration measurement, an oxygen saturation measurement, a blood pressure measurement, skin temperature, S-T segment elevation/depression, heart rate variability and respiration rate. Other parameters which can be used are any physical marker or physiological signal or indicator, including, but not limited to:
Physical Signals
Height, Weight, Age (Physical, Mental), Sex, History, Drugs/Medications in use, Body mass index, Body fat, Ethnic origin, Strength, Recovery times after exercise, Endurance/stamina, Cardiovascular function, Coordination, Flexibility, I.Q., Colour (Skin pallor, Retinal), Speech, Skin elasticity, Skin texture, Rashes, Swelling, Oedema, Pain, Shock, Nutritional status, State of hydration, Fatigue, Previous history.
Physiological Signals
EEG (Electrical (frontal, central, mastoid etc), MEG), Heart, Electrical—ECG, Sound, Pressure, Heart rate, Heart rate variability, Cardiac ejection fraction, Cardiac Output Respiration (Rate, Volume, Flow, Pressure, Phase, FEV1 (forced expiratory volume in one second), Gas levels), Blood pressure, (Invasive: Arterial, Central venous, Left atrial, Pulmonary capillary wedge, Right atrial, Pulmonary artery, Left ventricular, Right ventricular, Intra-cranial, Non-invasive, Pulmonary sounds, Pulse transit time, Pulse strength, Pulse rate, Pulse rhythm, Arterial blood oxygen saturation, Venous blood oxygen saturation, CO2 levels in blood, Impedance pneumography, Snoring, Temperature (Core, Peripheral, Blood, Lip), EMG, EOG, Movement (Gait, D.T's, Limb), Sight, Hearing, Smell, Taste, Touch, Throat microphone, Bowel sounds, Doppler ultrasound, Nerves.
Biochemical Signals
Glucose, Insulin, Lactate, Gas levels (Blood, Lungs), Hormones, Alcohol, Thyroid, Blood, Urine, Saliva, Sputum, Stools, Enzymes, Sweat, Interstitial fluid, Cells, Tissue, Hair follicles, ‘Recreational’ drugs, Proteins, Cholesterol, HIV.
Imaging Signals
Images of, for example:
Brain, Heart/cardiovascular system, Central nervous system, Internal organs Peripheral limbs, Bones.
The dimensionality reduction mapping may be, for instance, a distance preserving mapping or Principal Components Analysis (PCA). Other dimensionality reduction mappings are known. By “distance-preserving mapping” is meant a mapping which preserves some aspect of the geometrical relationship between the data points in the measurement space and in the visualisation space. Thus some aspect of the topology of the measurement space is preserved in the visualisation space. For instance, the mapping can minimise the difference in inter-point distance between pairs of points in the measurement space and the corresponding pairs of points in the visualisation space. An example of such a mapping, which matches the inter-point distances as closely as possible, is a development of Sammon's mapping as described in “Shadow Targets: A Novel Algorithm For Topographic Projections By Radial Basis Functions” by Tipping and Lowe (Artificial Neural Networks, Cambridge 7 to 9 Jul. 1997, IEE conference publication number 440). The distance measure may be any suitable measure, such as the Euclidian distance measure.
Preferably the parameters are normalised prior to mapping, so that the displayed visualisation space spans the desired extent of the measurement space, e.g. to take account of the fact that the different parameters are expressed in different units (for example, temperature in fractions of degrees and blood pressure in terms of mm Hg). The parameters may be normalised using a zero mean, unit variance transformation calculated over the data from the patient (where it is available) or example data from a patient group or another patient, or alternatively the parameters may be normalised using an empirical transformation based on the clinician's knowledge of the significance of changes of different magnitude in the various parameters.
One advantage of using a zero-mean, unit variance transformation is that if a signal drops-out or has to be omitted, e.g. because of excessive noise, it can be replaced by a zero value.
The visualisation space is preferably two-dimensional (i.e. m=2), in which case the display is a straightforward two-axis graphical display on arbitrary axes.
However, a three-dimensional visualisation space, or its representation on a screen is also possible.
The artificial neural network may be trained with data comprising a plurality of sets of parameters from the particular patient being monitored, or by data from a group of patients. Preferably the group is a group of patients with a similar condition to the patient being monitored because “normality” and “abnormality” for a typical patient with heart disease is radically different from “normality” for a patient with a different medical condition, or indeed a healthy person. Obviously when a patient is first-monitored there is insufficient data to train the neural network with data from that particular patient, thus there may be no alternative but to use a neural network trained on a group of patients. Subsequently, after enough data has been collected for that patient, a neural network may be trained with that data, to provide a more personalised mapping.
The data for training the artificial neural network may be selected by pre-clustering the data points in the measurement space. In other words, in a typical situation there may be too many data points for allowing training within a reasonable time period, and instead clusters of data points can be identified and the centres of the clusters used as nominal data points (prototypes) for training the network. Typically, there may be thousands or tens of thousands of data points for continuous monitoring over 24 hours or more for a patient or group of patients. The number of centres or prototypes will typically be greater than 100 but less than 1,000. After the network has been trained, the complete set of data points may be passed through the network to display change in patient condition over the course of collection of all of the data. One way of clustering the data and finding the centres or prototypes is, for instance, the k-means method.
The invention may be applied to human or animal patients, and may be applied to patients having a variety of conditions including disease or injury (actual or suspected), pre and post-operative care, monitoring during traumatic procedures monitoring of the elderly and/or infirm, neonatal monitoring or indeed monitoring in any medical or veterinary environment. The invention may be applied to monitoring in a medical or veterinary establishment or in the home. Thus it may be used as a health monitor in which readings may regularly be taken, and sent automatically to a central collection point for review. The readings may be sent only if they are outside a predefined region of “normality”.
The output of the neural network may be used to control automatically the management of the patient, e.g. the administration of drugs, to keep the patient's condition within the predefined region, e.g. the normal region. In a further enhancement, aspects of the management of the patient, e.g. the rate or amount of a drug being administered, or aspects of the environment, may be included as input parameters.
The invention may be embodied by a computer program running on a suitably programmed computer system, or by dedicated systems. Thus the invention extends to a computer program comprising program code means for executing some or all of the functionality of the invention, to a computer storage medium storing such a computer program, and to a programmed computer system embodying the invention.